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EP2639774A1 - Détection de caractéristiques de sécurité sur des documents de sécurité - Google Patents

Détection de caractéristiques de sécurité sur des documents de sécurité Download PDF

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Publication number
EP2639774A1
EP2639774A1 EP13172172.2A EP13172172A EP2639774A1 EP 2639774 A1 EP2639774 A1 EP 2639774A1 EP 13172172 A EP13172172 A EP 13172172A EP 2639774 A1 EP2639774 A1 EP 2639774A1
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EP
European Patent Office
Prior art keywords
security
features
image
wavelet
documents
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
EP13172172.2A
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German (de)
English (en)
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EP2639774B1 (fr
Inventor
Volker Lohweg
Eugen Gillich
Johannes Georg Schaede
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
KBA Notasys SA
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KBA Notasys SA
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Priority claimed from EP07109470A external-priority patent/EP2000992A1/fr
Application filed by KBA Notasys SA filed Critical KBA Notasys SA
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Publication of EP2639774A1 publication Critical patent/EP2639774A1/fr
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/20—Testing patterns thereon
    • G07D7/2016—Testing patterns thereon using feature extraction, e.g. segmentation, edge detection or Hough-transformation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G06T7/001—Industrial image inspection using an image reference approach
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/003—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency using security elements
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/20—Testing patterns thereon
    • G07D7/202—Testing patterns thereon using pattern matching
    • G07D7/205—Matching spectral properties

Definitions

  • the present invention generally relates to the authentication of security documents, in particular of banknotes. More precisely, the present invention relates to a method for checking the authenticity of security documents, in particular banknotes, wherein authentic security documents comprise security features printed, applied or otherwise provided on the security documents, which security features comprise characteristic visual features intrinsic to the processes used for producing the security documents.
  • the invention further relates to a digital signal processing unit adapted for carrying out part of the authentication method, a device for carrying out the authentication method, a method for producing security documents aimed at optimising the authentication of the security documents according to the authentication method, as well as to a method for detecting security features printed, applied or otherwise provided on security documents, in particular banknotes.
  • Offset-printed forgeries or "offset counterfeits" printed using commercial offset printing presses do also exist. These counterfeits are often printed in screen offset (i.e. with multicolour screen or raster combinations that are characteristic of commercial offset printing) and/or line offset (i.e. without any screen or raster combinations).
  • banknotes combine high quality printed features created by intaglio printing, line offset printing with high precision recto-verso register, and letterpress printing.
  • Intaglio and line offset in particular allow the creation of high resolution patterns with great print sharpness.
  • Letterpress printing is typically used for printing variable information, such as serial numbers. Further printing or processing techniques are also exploited to print or apply other features on banknotes, such as silk-screen printing, foil stamping, laser marking or perforating, etc.
  • Machine-based authentication of security documents i.e. automatic recognition in document processing systems such as vending machines, automatic teller machines (ATM), note acceptors and similar financial transaction machines, is also affected by counterfeiting. Indeed, it is not unusual to discover rather more advanced forgeries of security documents which also replicate the machine-readable security features present on genuine documents, such as infrared, luminescent and/or magnetic markings. As a matter of fact, most machine-based authentication systems essentially focus on such machine-readable features and do not or barely proceed to an actual visual inspection of the visible security features printed, applied or otherwise provided onto the security documents.
  • ISARD I ntaglio S canning A nd R ecognition D evice and is based on a measurement of the characteristic relief profile of intaglio-printed features.
  • the ISARD authentication principle and a device for carrying out this principle are also disclosed in patent publications GB 1 379 764 (corresponding to NL 7017662 ), NL 7410463 , NL 9401796 and NL 9401933 .
  • ISARD International Rectaglio imprints
  • ISARD technology was for instance applied as a pattern of parallel intaglio-printed lines on the Dutch 50 guilder "Sunflower” note (issued in 1982), as well as on the current issue of Euro banknotes (see [Hei06]).
  • the ISARD was and is mainly exploited by the public at large to perform a nail scratching test (i.e. by scratching a nail over the pattern of parallel intaglio lines).
  • EP 1 864 825 A1 (which corresponds to the entry into the European phase of International application No. WO 2006/106677 A1 ) discloses a printed product and method for extracting information from the printed product wherein information is embedded (or coded) in a printed design, especially a guilloche pattern, in such a way that this information can be detected by subjecting a sample image of the pattern to a Fourier transform. Coding of the information is achieved by spatially modulating the spacing between parallel/concentric curvilinear image elements.
  • Such spatial modulation leads to the production of spectral peaks in the Fourier-transformed spectral image of a sample image of the pattern, which spectral peaks are indicative of the information embedded in the printed design and can thus be decoded. More precisely, according to European patent application EP 1 864 825 A1 , the encoded information is extracted by looking at the spectral peak intensities.
  • EP 1 864 825 A1 A disadvantage of this approach resides in the fact that a specific coding must be embedded in a particular way in the printed patterns to permit decoding. This accordingly imposes substantial restrictions upon the designer who must follow specific design rules to design the printed patterns.
  • teaching of European patent application EP 1 864 825 A1 is basically limited to the embedding of information in guilloche patterns as this can readily be seen from looking at the Figures of EP 1 864 825 A1 .
  • European patent application EP 1 864 825 A1 is for instance applied with a view to encode information on a personal certificate (such as an identity card, driver licence, or the like), which information relates to the owner/bearer of the personal certificate.
  • the owner-dependent information is encoded into a guilloche pattern printed onto the personal certificate. This accordingly makes it more difficult for counterfeiters to produce similar personal certificates as the information embedded in the guilloche pattern is user-dependent.
  • any copy of the personal certificate produced at a similar resolution as the original will exhibit exactly the same information as the original.
  • This approach is thus mainly suitable for the purpose of authenticating security documents intended to bear user-dependent information (which is not the case of banknotes for instance).
  • US patent No. 5,884,296 discloses a device for discriminating an attribute of an image in a block area contained in a document image, which device involves performing a Fourier transformation based on image data in the block area and determining a spatial frequency spectrum relating to the image in the block area.
  • a neural network is exploited to output a discrimination result as to whether or not the attribute of the image in the block area is a halftone dot image based on the spatial frequency spectrum outputted from the Fourier transformation.
  • This device is in particular intended to be used in digital copying machines for the purpose of improving image quality.
  • 5,884,296 is more particularly intended to be used in the context of the copying of documents containing a mixture of text images, photographic images and/or dot images, which attributes needs be processed separately to yield good image quality in the copied documents.
  • US patent No. 5,884,296 does not in any way deal with the issue of authenticating security documents, but rather relates to a solution aimed at improving the discrimination between different attributes of an image.
  • European patent application No. EP 1 484 719 A2 discloses a method for developing a template of a reference document, such as a banknote, and using that template to validate other test documents, especially for validating currency in an automated teller machine.
  • the method involves using images of a plurality of reference documents, such as genuine banknotes, and segmenting each image in a like manner into a plurality of segments. Each segment is classified using a one-class classifier to determine a reference classification parameter. These parameters are used to define a threshold reference classification parameter. Validation of test documents is thus performed by comparing images of the test documents with the generated template rather than by looking at the intrinsic features of the test documents.
  • a general aim of the invention is therefore to improve the known methods for checking the authenticity of security documents, in particular banknotes.
  • a further aim of the invention is to provide a method that exploits the intrinsic features of the security features that are already typically printed, applied or otherwise provided on the security documents, especially the intrinsic features of intaglio-printed patterns.
  • a further aim of the present invention is to provide a solution that enables a robust and efficient differentiation between authentic (genuine) security documents and copies or counterfeits thereof.
  • Still another aim of the present invention is to provide a solution that can be implemented in automatic document processing systems (such as vending machines, ATMs, etc.) in a more simple manner than the currently known solutions.
  • automatic document processing systems such as vending machines, ATMs, etc.
  • a method for checking the authenticity of security documents in particular banknotes, wherein authentic security documents comprise security features printed, applied or otherwise provided on the security documents, which security features comprise characteristic visual features intrinsic to the processes used for producing the security documents, the method comprising the steps of :
  • the digital processing of the sample image includes (i) performing a transform of the sample image to derive at least one set of spectral coefficients representative of the high resolution details of the sample image at a fine scale, and (ii) processing the spectral coefficients to extract the classifying features.
  • the transform is a wavelet-transform, advantageously a discrete wavelet transform (DWT) selected from the group comprising for instance Haar-wavelet transform, Daubechies-wavelet transform, and Pascal-wavelet transform. Any other suitable wavelet transform or derivative thereof could be used.
  • DWT discrete wavelet transform
  • the processing of the spectral coefficients preferably includes performing a processing of the statistical distribution of the spectral coefficients.
  • This statistical processing can in particular include the computing of at least one statistical parameter selected from the group comprising the arithmetic mean (first moment in statistics), the variance (second moment in statistics), the skewness (third moment in statistics), the excess (fourth moment in statistics), and the entropy of the statistical distribution of said spectral coefficients.
  • the decomposition of the sample image is advantageously performed as a result of one or more iterations of a multiresolution analysis (MRA) of the sample image.
  • MRA multiresolution analysis
  • a method for checking the authenticity of security documents in particular banknotes, wherein authentic security documents comprise security features printed, applied or otherwise provided on the security documents, which security features comprise characteristic visual features intrinsic to the processes used for producing the security documents, the method comprising the step of digitally processing a sample image of at least one region of interest of the surface of a candidate document to be authenticated, which digital processing includes performing one or more iterations of a multiresolution analysis of the sample image.
  • the above methods may provide for the digital processing of a plurality of sample images corresponding to several regions of interest of the same candidate document.
  • the sample image can be acquired at a relatively low-resolution, i.e. lower than 600 dpi, preferably of 300 dpi.
  • a relatively low-resolution i.e. lower than 600 dpi, preferably of 300 dpi.
  • Tests have indeed shown that a high scanning resolution for the sample image is not at all necessary. This is particularly advantageous in that the low resolution shortens the time necessary for performing the acquisition of the sample image and reduces the amount of data to be processed for a given surface area, which accordingly substantially facilitates a practical implementation of the method.
  • the security features that are exploited for the purpose of authentication preferably mainly include intaglio patterns.
  • the security features may include intaglio patterns, line offset patterns, letterpress patterns, optically-diffractive structures (i.e. patterns or structures that are intrinsic to the processes carried out by the security printer) and/or combinations thereof.
  • Maximization of the authentication rating is achieved by ensuring that the selected region of interest includes a high density (high spatial frequency) of patterns (preferably linear or curvilinear intaglio-printed patterns).
  • the patterns can in particular be patterns of a pictorial representation, such as a portrait, provided on the candidate document.
  • a digital signal processing unit for processing image data of a sample image of at least one region of interest of the surface of a candidate document to be authenticated according to the above method, the digital signal processing unit being programmed for performing the digital processing of the sample image, which digital signal processing unit can advantageously be implemented in an FPGA (Field-Programmable-Gate-Array) unit.
  • FPGA Field-Programmable-Gate-Array
  • a device for checking the authenticity of security documents in particular banknotes, according to the above method, comprising an optical system for acquiring the sample image and a digital signal processing unit programmed for performing the digital processing of the sample image.
  • a method for producing security documents comprising the step of designing security features to be printed, applied, or otherwise provided on the security documents, wherein the security features are designed in such a way as to optimise an authenticity rating computed according to the above method by producing a characteristic response in the said at least one scale sub-space.
  • a method for detecting security features printed, applied or otherwise provided on security documents comprising the step of digitally processing a sample image of at least one region of interest of the surface of a candidate document, which region of interest is selected to include at least a portion of said security features, which digital processing includes performing one or more iterations of a multiresolution analysis of the sample image to extract classifying features which are characteristic of said security features.
  • This method is in particular advantageously applied for detecting intaglio-printed patterns.
  • the present invention stems from the observation that security features printed, applied or otherwise provided on security documents using the specific production processes that are only available to the security printer, in particular intaglio-printed features, exhibit highly characteristic visual features (hereinafter referred to as "intrinsic features ”) that are recognizable by a qualified person having knowledge about the specific production processes involved.
  • FIG 1a is a greyscale scan of an illustrative banknote specimen 1 showing the portrait of Jules Verne which was produced during the year 2004 by the present Applicant.
  • This banknote specimen 1 was produced using a combination of printing and processing techniques specific to banknote production, including in particular line offset printing for printing the multicolour background 10 of the note, silk-screen printing for printing optically-variable ink patterns, including motifs of a planisphere 20 and of a sextant 21, foil stamping techniques for applying optically-variables devices, including a strip of material 30 carrying optically-diffractive structures extending vertically along the height of the banknote (which strip 30 is schematically delimited by two dashed lines in Figure 1a ), intaglio printing for printing several intaglio patterns 41 to 49, including the portrait 41 of Jules Verne, letterpress printing for printing two serial numbers 51, 52, and varnishing for varnishing the note with a layer of protective varnish.
  • This banknote specimen 1 is also provided with a marking 60 on the right-hand side of the specimen, which marking 60 is applied by partial laser ablation of the strip 30 and of an underlying layer of offset-printed ink (not referenced).
  • the portrait 41 (together with the vertical year designation 2004 and the pictorial motifs surrounding the portrait), a logo of "KBA-GIORI” with the Pegasus 42, indications "KBA-GIORI” 43 and “Specimen” 44, and tactile patterns 45 to 49 on three corners of the note and on the right-hand side and left-hand side of the note were printed by intaglio printing on top of the line offset background 10, the silk-screen-printed motifs 20, 21 and the strip of material 30.
  • serial numbers 51, 52 were printed and the varnishing was performed following the intaglio printing phase. It shall further be understood that the banknote specimen 1 was produced on sheet-fed printing and processing equipment (as supplied by the present Applicant), each printed sheet carrying an array of multiple banknote specimens (as is usual in the art) that were ultimately cut into individual notes at the end of the production process.
  • Figure 1b is a greyscale photograph of the upper right corner of the banknote specimen of Figure 1a showing in greater detail the intaglio-printed logo of "KBA-GIORI" with the Pegasus 42 and tactile pattern 45 which comprises a set of parallel lines at forty-five degrees partly overlapping with the Pegasus 42.
  • the characteristic embossing and relief effect of the intaglio printing as well as the sharpness of the print can clearly be seen in this photograph.
  • Figure 2a is a more detailed view of a left-hand side portion of the portrait 41 of Figure 1a (patterns 20, 21 and 44 being also partly visible in Figure 2a).
  • Figure 2b is an enlarged view of a square portion (or region of interest R.o.l.) of the portrait 41, which square portion is illustrated by a white square in Figure 2a.
  • Figure 2b shows some of the characteristic intrinsic features of the intaglio patterns constituting the portrait 41.
  • the region of interest R.o.l. used for subsequent signal processing does not need to cover a large surface area of the document. Rather, tests have shown that a surface area of less than 5 cm 2 is already sufficient for the purpose of the authentication.
  • Figures 3a, 3b and 4a, 4b are greyscale images similar to Figures 2a, 2b of two colour copies of the banknote specimen shown in Figure 1a , which copies were produced using commercial colour copying equipment.
  • the depicted white square indicates the corresponding region of interest R.o.l. of the portrait which is shown in enlarged view in Figures 3b and 4b , respectively.
  • the first colour copy illustrated in Figures 3a, 3b was produced using an Epson ink-jet printer and Epson photo-paper.
  • the second colour copy illustrated in Figures 4a, 4b was produced using a Canon ink-jet printer and normal paper. A high-resolution scanner was used to scan the original specimen and provide the necessary input for the ink-jet printers.
  • the decomposition of the image is carried out by performing digital signal processing techniques based on so-called wavelets ("ondelettes" in French).
  • a wavelet is a mathematical function used to divide a given function or signal into different scale components.
  • a wavelet transformation (or wavelet transform) is the representation of the function or signal by wavelets. Wavelet transforms have advantages over traditional Fourier transforms for representing functions and signals that have discontinuities and sharp peaks. According to the present invention, one in particular exploits the properties of so-called discrete wavelet transforms (DWTs) as this will be discussed in the following.
  • DWTs discrete wavelet transforms
  • Fourier transformation (as for instance used in the context of the solutions discussed in European patent application EP 1 864 825 A1 and US patent No. US 5,884,296 ) is not to be assimilated to wavelet transformation. Indeed, Fourier transformation merely involves the transformation of the processed image into a spectrum indicative of the relevant spatial frequency content of the image, without any distinction as regards scale.
  • Wavelet theory will not be discussed in-depth in the present description as this theory is as such well-known in the art and is extensively discussed and described in several textbooks on the subject. The interested reader may for instance refer to the following books and papers about wavelet theory :
  • a wavelet can conveniently be expressed by a wavelet function (or “mother wavelet”) ⁇ and a scaling function (or “father wavelet”) ⁇ .
  • the wavelet function ⁇ can in effect be expressed as a bandpass/high-pass filter which filters an upper half of the signal scale/spectrum, while the scaling function ⁇ can be expressed as a low-pass filter which filters the remaining lower half of the signal scale/spectrum.
  • This principle is schematically illustrated in Figure 5a as a one-level digital filter bank comprising a low-pass filter with function h(n) and a high-pass filter with function g(n) which split the signal scale/spectrum in two parts of equal spectral range.
  • the output y LOW (n) of the low-pass filter which basically is the result of the convolution * of signal x(n) and function h(n), comprises the scaling function transform coefficients, or simply "scaling coefficients" (also referred to as the approximation coefficients), while the output y HIGH (n) of the high-pass filter, which is similarly the result of the convolution * of signal x(n) and function g(n), comprises the wavelet function transform coefficients, or simply "wavelet coefficients" (also referred to as the detail coefficients).
  • a signal can be decomposed into a plurality of wavelet coefficients corresponding to different scales (or resolutions) by iteratively repeating the process, i.e. by passing the approximation coefficients outputted by the low-pass filter to a subsequent similar filter stage.
  • This approach is known as a multiresolution analysis or MRA (see [Mal89]) and is schematically illustrated in Figure 5b in the case of a three-level multiresolution analysis.
  • MRA multiresolution analysis
  • Figure 5b the filter bank is in effect a three-level filter bank wherein the low-pass filtered output of a preceding filter stage is again filtered by the subsequent filter stage.
  • the signal x(n) is in effect decomposed in four signal components corresponding to three distinct scales, namely (i) detail coefficients at a first scale (the level 1 coefficients) which comprise half the number of samples as compared to signal x(n), (ii) detail coefficients at a second scale different from the first (the level 2 coefficients) which comprise 1/4 of the number of samples as compared to signal x(n), and (iii) approximation coefficients and (iv) detail coefficients at a third scale (the level 3 coefficients) which each comprise 1/8 of the number of samples as compared to signal x(n).
  • a discrete sample signal can eventually be completely decomposed in a set of detail coefficients (wavelet coefficients) at different scales as long as the sample signal includes 2 N samples, where N would be the number of iterations or levels required to completely decompose the signals into wavelet coefficients.
  • multiresolution analysis refers to a signal processing technique based on wavelet transforms, whereby a signal is decomposed in a plurality of nested subspaces of different scales ranging from fine details (high resolution components) to coarse details (low resolution components) of the signal as schematically illustrated by the diagram of Figure 6 .
  • the intrinsic features of genuine security features especially the intrinsic feature of intaglio patterns, will be identified by looking especially at the fine high resolution (fine scale) details of an image of the candidate document to be authenticated, rather than at the coarser low resolution details of the image of the candidate document.
  • Two-dimensional wavelet transform basically involves a row-wise and column-wise processing of the two-dimensional signal wherein the rows and columns of the signal are processed separately using the above-discussed one-dimensional wavelet algorithm. This will be explained in reference to Figures 7a, 7b , 8 and 9 .
  • an original image i.e. an image corresponding to a selected region of interest of a sample image of a candidate document to be authenticated - such as for instance the image of Figures 2b , 3b or 4b
  • This original image c 0 consists of a matrix of n x n pixels, where n is dividable by 2 N , N being an integer corresponding to the number N of wavelet iterations one wishes to perform.
  • the image size should be sufficiently big so as to encompass a relatively high number of features.
  • the original image c 0 may for instance consist of a matrix of 256 x 256 pixels. Other images sizes are however perfectly possible. At a sampling resolution of 300 dpi, it will be appreciated that such an image size corresponds to a surface area on the candidate document to be authenticated of approximately 2 x 2 cm 2 .
  • the original image c 0 is decomposed in four sub-images c 1 , d 1 1 , d 2 1 and d 3 1 each having a size of (n/2) x (n/2) pixels.
  • Sub-image c 1 contains the approximation of the original image c 0 resulting from low-pass filtering along both the rows and columns of the original image c 0 .
  • sub-images d 1 1 , d 2 1 and d 3 1 contain the details of the original image c 0 resulting from high-pass filtering along the rows and/or columns of the original image c 0 . More precisely :
  • sub-images d 1 1 , d 2 1 and d 3 1 are representative of details of the image c 0 at a first resolution (or scale), while sub-images d 1 2 , d 2 2 and d 3 2 are representative of details of the image c 0 at a second resolution, half that of the first resolution.
  • sub-images d 1 m will each contain the horizontal details of the original image at different scales (or resolutions)
  • sub-images d 2 m and d 3 m will each respectively contain the vertical and diagonal details of the original image at different scales.
  • the two-dimensional wavelet transform is preferably carried out according to the so-called "non-standard decomposition" method, which method is schematically illustrated in Figure 8 .
  • this decomposition method one-dimensional wavelet transform is alternately performed on the rows and the columns of the image.
  • references A, D, a, d respectively designate :
  • the rows of the original image are first processed and then the columns, such as to yield to the result illustrated in Figure 7a (where Aa, Da, Ad and Dd respectively correspond to sub-images c 1 , d 1 1 , d 2 1 and d 3 1
  • sub-image Aa (which corresponds to sub-image c1) is similarly processed starting with the rows and then the columns, resulting in the same decomposition as illustrated in Figure 7b (where AaAa, AaDa, AaAd and AaDd respectively correspond to sub-images c 2 , d 1 2 , d 2 2 and d 3 2 ).
  • non-standard decomposition An alternative to the above-discussed "non-standard decomposition” method is the so-called “standard decomposition” method which is carried out by performing all required iterations along the rows and then only the required iterations along the columns. This method is schematically illustrated in Figure 9 .
  • non-standard decomposition necessitates more memory accesses in comparison to the other method, it has the advantage that it requires less computation time, since, during each iteration, only a quarter of the data resulting from the preceding iteration has to be processed. Furthermore, the horizontal and vertical details are extracted separately by means of the "non-standard decomposition" method as this can be readily understood from comparing Figures 8 and 9 .
  • DCTs discrete wavelet transforms
  • Successful tests have in particular been carried out by making use of the so-called Haar-, Daubechies- and Pascal-wavelet transforms which are known as such in the art.
  • the Haar-wavelet transform is actually the first known wavelet transform. This wavelet transform (while not designated as such at the time) was discovered in 1909 by Hungarian mathematician Alfred Haar. This wavelet transform is also known as a special case of the so-called Daubechies-wavelet transform.
  • the Daubechies-wavelet transform (see [Dau92]) is named after Ingrid Daubechies, a Belgian physicist and mathematician.
  • the Daubechies-wavelets are a family of orthogonal wavelets and are characterised by a maximal number of so-called vanishing moments (or taps).
  • Daubechies 4 tap wavelet or db4 transform
  • An advantage of the Daubechies-db4 transform over the Haar-wavelet transform resides in particular in the increased filtering efficiency of the Daubechies transform, i.e. the cut-off frequencies of the low-pass and high-pass filters are more sharply defined.
  • the Pascal-wavelet transform is based on the binomial coefficients of Pascal's triangle (named after the French philosopher and mathematician Blaise Pascal). Although the Pascal-wavelet transform has less sharply-defined cut-off frequencies than the Haar- and Daubechies wavelet transforms, this transform can better approximate continuous signals than the Haar-wavelet transform and requires less computation time than the Daubechies-wavelet transform.
  • the Pascal-wavelet transform is a non-orthogonal wavelet.
  • the signal i.e. the image data of the region of interest
  • the signal does not need to be completely decomposed into wavelet components. Accordingly, it suffice to perform one or more iterations of the wavelet transformation of the image data in order to extract the relevant features that will enable to built representative classifying data about the candidate document to be authenticated, as this will be appreciated from the following.
  • the most relevant scales of the image to be considered are those corresponding to the fine, high resolution details which are first derived in the course of the multiresolution analysis.
  • the wavelet transform i.e. a one-level resolution analysis as schematically illustrated by Figure 5a
  • the sample image may simply be decomposed into at least one fine scale sub-space containing high resolution details of the sample image.
  • Figure 10a illustrates the result of the first iteration of a two-dimensional wavelet transform as applied to the image shown in Figure 2b of an original banknote specimen.
  • the original image had a size of 252 x 252 pixels and use was made of the Haar-wavelet transform mentioned above to process the image.
  • the approximation image c 1 resulting from low-pass filtering is shown in the upper left corner of Figure 10a .
  • the detail images d 1 1 , d 2 1 and d 3 1 resulting from high-pass filtering are shown as substantially dark regions, due to the fact that the wavelet coefficients have small values and also include negative coefficients (the wavelet coefficients therefore appear as substantially "black" pixels when directly visualized).
  • the images can be normalized so that the coefficients are comprised within the range of values 0 to 255 (i.e. the 8-bit value range of a greyscale image).
  • d 1 1 N , d 2 1 N and d 3 1 N respectively designate normalized versions of detail images d 1 1 , d 2 1 and d 3 1
  • the wavelet-transform adequately detects the sharp transitions of the intaglio patterns.
  • Figure 11a shows a normalized image d G 1 N resulting from the combination of the three detail images d 1 1 , d 2 1 and d 3 1 of Figures 10a , 10b .
  • Figures 11b and 11c illustrate the corresponding normalized image d G 1 N obtained as a result of the wavelet transform of the images of the first and second colour copies of Figures 3b and 4b , respectively.
  • Figure 12 is an illustration of nine histograms showing the statistical distributions of the wavelet coefficients for the horizontal, vertical and diagonal details (i.e. the wavelet coefficients of detail images d 1 1 , d 2 1 and d 3 1 for each one of the images of Figures 2b , 3b and 4b . More precisely, the left, middle and right columns of Figure 12 respectively show the corresponding histograms derived for the images of Figures 2b , 3b and 4b , while the upper, middle and bottom rows of Figure 12 respectively shown the corresponding histograms for the horizontal, vertical and diagonal details.
  • the histograms derived from the image of the original specimen are wider than the histograms derived from the images of the colour copies (middle and right columns in Figure 12 ).
  • the variance ⁇ 2 i.e. the measure of the dispersion of the wavelet coefficients, can conveniently be used to categorize the statistical distribution of the wavelet coefficients.
  • the variance ⁇ 2 is also referred to in statistics as the "second moment".
  • the above-listed moments shall be normalized to enable proper comparison and classification of the various candidate documents.
  • Figure 13 illustrates the notions of skewness and excess.
  • a "positive skewness” (as illustrated) is understood to characterize a statistical distribution wherein the right tail of the distribution is longer and wherein the "mass" of the distribution is concentrated on the left. The converse is a “negative skewness”.
  • a “positive/high excess” or “negative/low excess” (as illustrated) is understood to characterize a statistical distribution comprising a sharper peak and fatter tails, respectively a more rounded peak and wider "shoulders”.
  • Figures 14a to 14c are three bar charts illustrating the variance ⁇ 2 of the statistical distributions of the wavelet coefficients illustrated by the diagrams of Figure 12 .
  • Reference numerals 1, 2, 3 in Figures 14a to 14c respectively refer to the three candidate documents that have been processed, namely the original specimen ( Figures 2a and 2b ), the first colour copy ( Figures 3a and 3b ) and the second colour copy ( Figures 4a and 4b ).
  • the variance ⁇ 2 is shown for the horizontal details
  • Figures 14b and 14c respectively show the variance ⁇ 2 for the vertical and diagonal details.
  • the variance ⁇ 2 is substantially higher in the case of the distribution of the wavelet coefficients deriving from the image of the original specimen than that computed from the statistical distributions of the wavelet coefficients deriving from the images of the colour copies.
  • Tests have been carried out on various original (i.e. authentic) specimens of banknotes and colour copies (i.e. counterfeits) thereof. These tests have shown that the method according to the present invention is very robust, especially when the image data of the region of interest being processed contains a relatively high density of intaglio-printed features, such as in the case of a portion of the portrait or of any other similarly dense pictorial representation that can be found on most banknotes (such as the intaglio-printed patterns representing architectural objects on the Euro banknotes). The tests have also shown that areas containing a lesser amount of intaglio feature still lead to good results.
  • Figures 15a and 15b are two enlarged views of a part of the intaglio-printed portrait of Bettina von Arnim as it appears on the recto side of the DM 5 banknote which was issued during the years 1991 to 2001 in Germany prior to the introduction of the Euro.
  • Figure 15b in particular shows an example of a possible region of interest that was exploited for the purpose of authentication according to the above-described method.
  • FIG 16a shows for the purpose of illustration six similar images of the same region of interest taken from an original specimen in very good condition (illustration A), an original specimen with a relatively high degree of wear (illustration B), a colour-copy produced by inkjet printing on photo-quality paper at a resolution of 5600 dpi (illustration C), a colour-copy produced by inkjet printing on normal paper at a resolution of 5600 dpi (illustration D), a colour-copy produced by thermo-sublimation on photo-quality paper at a resolution of 300 dpi (illustration E) and a colour-copy produced by laser printing on normal paper at a resolution of 1200 dpi (illustration F).
  • Figure 16b shows the corresponding histograms of the statistical distributions of the wavelet coefficients (in Figure 16b the histograms are derived from the combination of the three detail images resulting from low-pass filtering of the images of Figure 16a ).
  • the histograms computed from the images of the two original specimens are highly similar, despite the different degrees of wear of the specimens (and the presence of a wrinkle in the region of interest of the image of the second original specimen - see image B in Figure 16a ).
  • the statistical distribution of the wavelet coefficients derived from the image of the two inkjet-printed copies and the thermo-sublimation copy (histograms C to E) are clearly different.
  • Figure 17 shows the superposition of the histograms corresponding to the first original specimen (histogram A in Figure 16b ) and to the laser-printed colour copy (histogram F in Figure 16b ).
  • Figure 18a and 18b are two bar charts illustrating the variance ⁇ 2 and the excess C, respectively, computed from the statistical distribution of the wavelet coefficients derived from images of substantially the same region of interest of eleven candidate documents comprising five original specimens with different degrees of wear (candidates 1 to 5) and six colour copies (candidates 6 to 11) produced by inkjet-printing, thermo-sublimation, or colour-laser-printing.
  • the variance ⁇ 2 and the excess C clearly show that a distinction between the authentic documents and the counterfeits is possible using these two statistical parameters as classifying data.
  • Figure 19 is an illustration of a corresponding feature space using the variance ⁇ 2 and the excess C as (X; Y) coordinates in the feature space, where the results derived from candidate documents can be positioned.
  • a borderline can clearly be drawn between the points corresponding to original specimens (located on the upper right corner of the feature space) and those corresponding to colour copies (located on the lower left corner of the feature space).
  • Figure 20 is a view of a feature space similar to that of Figure 19 where the variance ⁇ 2 and the excess C are again used as (X; Y) coordinates and which shows the results that were obtained by processing additional candidate documents, including original Euro banknotes. These results confirm the robustness and efficiency of the authentication method according to the present invention.
  • the method according to the invention does not as such require that the selected region of interest be strictly one and a same area of the candidate documents. As a matter of fact, deviations regarding the actual position of the region of interest from one candidate document to another do not substantially affect the results.
  • the method according to the present invention is accordingly also advantageous in that it does not require precise identification and positioning of the region of interest prior to signal processing. This greatly simplifies the whole authentication process and its implementation (especially in ATM machines and the like) as one merely has to ensure that the selected region of interest more or less covers an area comprising a sufficiently representative amount of intrinsic features (in particular intaglio features).
  • FIG 21 schematically illustrates an implementation of a device for checking the authenticity of security documents, in particular banknotes, according to the above-described method.
  • This device comprises an optical system 100 for acquiring a sample image (image c 0 ) of the region of interest R.o.l. on a candidate document 1 to be authenticated, and a digital signal processing (DSP) unit 200 programmed for performing the digital processing of the sample image.
  • the DSP 200 may in particular advantageously be implemented as a Field-Programmable-Gate-Array (FPGA) unit.
  • FPGA Field-Programmable-Gate-Array
  • security features in particular intaglio-printed patterns
  • security documents in particular banknotes
  • security features comprise characteristic visual features intrinsic to the processes used for producing the security documents.
  • the classifying features may conveniently be statistical parameters selected from the group comprising the arithmetic mean, the variance ( ⁇ 2 ), the skewness, the excess (C), and the entropy of the statistical distribution of spectral coefficients representative of high resolution details of the sample image at a fine scale.
  • an authenticity rating computed according to the above described method can be optimised by designing the security features that are to be printed, applied, or otherwise provided on the security documents in such a way as to produce a characteristic response in the scale sub-space or sub-spaces containing high resolution details of the sample image that is processed.
  • Such optimisation can in particular be achieved by acting on security features including intaglio patterns, line offset patterns, letterpress patterns, optically-diffractive structures and/or combinations thereof.
  • security features including intaglio patterns, line offset patterns, letterpress patterns, optically-diffractive structures and/or combinations thereof.
  • a high density of such patterns preferably linear or curvilinear intaglio-printed patterns, as shown for instance in Figure 2b , would in particular be desirable.
  • the authentication principle is preferably based on the processing of an image containing (or supposed to be containing) intaglio-printed patterns
  • the invention can be applied by analogy to the processing of an image containing other security features comprising characteristic visual features intrinsic to the processes used for producing the security documents, in particular line offset patterns, letterpress patterns, optically-diffractive structures and/or combinations thereof.
  • wavelet transform has been discussed in the context of the above-described embodiments of the invention, it shall be appreciated that this particular transform is to be regarded as a preferred transform within the scope of the present invention.
  • Other transforms are however possible such as the so-called chirplet transform. From a general point of view, any suitable transform can be used as long as it enables to perform a decomposition of the sample image into at least one scale sub-space containing high resolution details of the sample image.
  • the above-described methodology can be applied in such a may as to decompose the sample image into more than one scale sub-space containing high resolution details of the sample image at different scales.
  • classifying features could be extracted from each scale sub-space in order to characterize the candidate document being authenticated.
  • the present invention is not limited to the decomposition of the sample image into only one scale sub-space containing high resolution details of the sample image.

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computer Security & Cryptography (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Quality & Reliability (AREA)
  • Theoretical Computer Science (AREA)
  • Inspection Of Paper Currency And Valuable Securities (AREA)
  • Image Analysis (AREA)
  • Editing Of Facsimile Originals (AREA)
  • Image Processing (AREA)
EP13172172.2A 2007-06-01 2008-06-02 Détection de caractéristiques de sécurité sur des documents de sécurité Active EP2639774B1 (fr)

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EP07109470A EP2000992A1 (fr) 2007-06-01 2007-06-01 Authentification de documents de sécurité, en particulier des billets de banque
EP07110633 2007-06-20
EP08751322.2A EP2153423B1 (fr) 2007-06-01 2008-06-02 Authentification de documents de sécurité, en particulier de billets de banque
EP13172172.2A EP2639774B1 (fr) 2007-06-01 2008-06-02 Détection de caractéristiques de sécurité sur des documents de sécurité
PCT/IB2008/052135 WO2008146262A2 (fr) 2007-06-01 2008-06-02 Authentification de documents de sécurité, en particulier de billets de banque

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EP08751322.2 Division 2008-06-02

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ZA200908451B (en) 2011-02-23
US20100195894A1 (en) 2010-08-05
CN101755290A (zh) 2010-06-23
CN101755290B (zh) 2012-11-28
WO2008146262A3 (fr) 2009-01-29
KR20100054115A (ko) 2010-05-24
MY183641A (en) 2021-03-04
HK1141355A1 (en) 2010-11-05
BRPI0812240B1 (pt) 2019-07-02
EP2153423A2 (fr) 2010-02-17
WO2008146262A2 (fr) 2008-12-04
AU2008256256B2 (en) 2014-05-08
KR101461208B1 (ko) 2014-11-18
EP2639774B1 (fr) 2018-09-26
AU2008256256A1 (en) 2008-12-04
RU2009147707A (ru) 2011-07-20
JP5314007B2 (ja) 2013-10-16
CA2689270A1 (fr) 2008-12-04
CN102930632A (zh) 2013-02-13
MX2009012963A (es) 2010-02-12
CA2689270C (fr) 2016-07-12
EP2639773A1 (fr) 2013-09-18
BRPI0812240A2 (pt) 2014-12-23
NZ581538A (en) 2012-08-31
EP2639773B1 (fr) 2018-09-26
JP2010530095A (ja) 2010-09-02
EP2153423B1 (fr) 2018-09-26
US8781204B2 (en) 2014-07-15

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